knitr::opts_chunk$set(echo = TRUE)
######DO NOT MODIFY. This will load required packages and data.

library(tidyverse)
library(gganimate)
library(gifski)
library(plotly)

cces <- drop_na(read_csv(url("https://www.dropbox.com/s/ahmt12y39unicd2/cces_sample_coursera.csv?raw=1")))

cel <- drop_na(read_csv(url("https://www.dropbox.com/s/4ebgnkdhhxo5rac/cel_volden_wiseman%20_coursera.csv?raw=1")))

Exercise 1

Data: cces

Explain what you are visualizing here: A bar chart shows the educational levels of respondents towards their job in the cces file

Put your figure here:

data1 <-cces
data1 <- data1 %>% 
  mutate(
    Gender = case_when(
      gender == 1 ~ "Male",
      gender == 2 ~ "Female"))

data1 <- data1 %>% 
  mutate(
    Educ = case_when(
      educ == 1 ~ "No high school",
      educ == 2 ~ "High school graduate",
      educ == 3 ~ "Some college",
      educ == 4 ~ "2-year",
      educ == 5 ~ "4-year",
      educ == 6 ~ "Post-grad"))
data1$Educ = factor(data1$Educ, levels = c ("No high school", "High school graduate", "Some college", "2-year", "4-year", "Post-grad"))

data1 <- data1 %>% 
  mutate(
    transCC18_308a = case_when(
      CC18_308a == 1 ~ "Strongly approve",
      CC18_308a == 2 ~ "Somewhat approve",
      CC18_308a == 3 ~ "Somewhat disapprove",
      CC18_308a == 4 ~ "Strongly disapprove"))
data1$transCC18_308a = factor(data1$transCC18_308a, levels = c ("Strongly disapprove", "Somewhat disapprove", "Somewhat approve", "Strongly approve"))


data1.1 <- data1 %>% count(Gender, Educ, transCC18_308a,  sort = TRUE)
colnames(data1.1) <- c("Gender", "Education", "Opinion", "Count")
data1.2 <- data1 %>% count(Gender, transCC18_308a,  sort = TRUE)
colnames(data1.2) <- c("Gender", "Opinion", "Count")

ggplot(data1.2, aes( y = Count, x = Gender, fill = Opinion, label = Count)) + 
  stat_summary(geom = "bar", position = "dodge") + # Cannot use geom_bar() 
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  theme(plot.title = element_text(hjust = 0.5)) + # Adjust the title to the center
  geom_text(position = position_dodge(width = .9), vjust = - 0.5, size = 3)  # Adjust the label to the center of each bar, vjust: adjust the height position of label  

a <- ggplot(data1.2, aes( y = Count, x = Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position = "dodge") +  
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  theme(plot.title = element_text(hjust = 0.5))
ggplotly(a)

We can see that there are 4 levels of opinions: strongly disapprove, disapprove, approve, strongly approve.For further analysis, the following graph will cover the Educational levels of respondents in each type of opinions:

ggplot(data1.1, aes( y = Count, x= Gender, fill = Opinion, label = Count)) + 
  stat_summary(geom = "bar", position = "dodge") + # Cannot use geom_bar
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  theme(plot.title = element_text(hjust = 0.5)) + # Adjust the title to the center
  geom_text(position = position_dodge(width = .9), vjust = - 0.5, size = 3) + # Adjust the label to the center of each bar, vjust: adjust the height position of label  
  facet_wrap(~Education) + ylim(0,80)  

a <- ggplot(data1.1, aes( y = Count, x= Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position = "dodge") + # Cannot use geom_bar
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  theme(plot.title = element_text(hjust = 0.5)) + # Adjust the title to the center
  facet_wrap(~Education)
ggplotly(a)

Next, I will illustrate the remaining types of bar chart which makes it easier to compare proportions: First chart shows the opinion of respondents on the scale of 1, second chart shows the number of respondents stack on one another. It’s a little bit difficult to calculate and show percentage label for 1st chart and 2nd chart. So I use the interactive charts.

ggplot(data1.1, aes( y = Count, x = Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position="fill") +
  labs( x = "Gender", y= "Proportion of respondents", title = "The opinion of respondents") +
  facet_wrap(~Education) +
  scale_y_continuous(labels = scales::percent_format()) +
  theme(plot.title = element_text(hjust = 0.5))  

ggplot(data1.1, aes( y = Count, x = Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position="stack") +
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  facet_wrap(~Education) +
  theme(plot.title = element_text(hjust = 0.5))

For interactive plot:

b <-   ggplot(data1.1, aes( y = Count, x = Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position="fill") +
  labs( x = "Gender", y= "Proportion of respondents", title = "The opinion of respondents") +
  facet_wrap(~Education) +
  scale_y_continuous(labels = scales::percent_format()) +
  theme(plot.title = element_text(hjust = 0.5))   
ggplotly(b)
b1 <- ggplot(data1.1, aes( y = Count, x = Gender, fill = Opinion)) + 
  stat_summary(geom = "bar", position="stack") +
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents") +
  facet_wrap(~Education) +
  theme(plot.title = element_text(hjust = 0.5))
ggplotly(b1)

Let try using animation graph

Explain what you are visualizing here: A bar chart shows the number of respondents education in the cces file

Put your figure here:

ggplot(data1.1, aes( y = Count, x= Gender, fill = Opinion, label = Count)) + 
  stat_summary(geom = "bar", position = "dodge") +  
  labs( x = "Gender", y= "Number of respondents", title = "The opinion of respondents", subtitle = 'Education: {closest_state}') +
  theme(plot.title = element_text(hjust = 0.5)) +  
  geom_text(position = position_dodge(width = .9), vjust = - 0.5, size = 4) +
  theme(
    plot.title =    element_text(size = (18), face = "bold" ),
    plot.subtitle = element_text(size = (15)),
    legend.title =  element_text(size = (15), face = "bold.italic", colour = "black"),
    legend.text =   element_text(size = (14), face = "italic",      colour = "black"),
    axis.title =    element_text(size = (15),                       colour = "black"),
    axis.text =     element_text(size = (13),                       colour = "black" ) ) +  
  transition_states(Education, transition_length = 0, state_length = 2) +
  enter_fade() +  exit_fade()

anim_save("The opinion of respondents.gif")

For interactive plot with animation:

plot_ly(data1.1) %>%
  add_bars(
    x = ~Gender, xend = ~Gender, 
    y = ~ Count, yend = ~0, 
    frame = ~Education,
    color = ~ Opinion  ) %>% 
  animation_slider( currentvalue = list(prefix = "Education: ", font = list(color="red"))) %>%
  animation_opts(1000, easing = "elastic", redraw = FALSE)

Exercise 2

Data: cel

Explain what you are visualizing here: Drawing a scatter plot illustrating the relationship between the DW-Nominate score of each member (dwnom1) and the number of the bills introduced in congress 110 (all_bills) according to years (elected)

Put your figure here:

data2 <- filter(cel, congress == 110)
data2 <- data2 %>% 
  mutate(
    Gender = case_when(
      female == 0 ~ "Male",
      female == 1 ~ "Female"))

ggplot(data2, aes(x= dwnom1, y = all_bills, colour = Gender)) +
  geom_point() +
  facet_wrap(~elected) +
  labs(x ="DW-Nominate score", y = "Number of bills", title = "The DW-Nominate score and Number of bills introduced each year") +
  theme(plot.title = element_text(hjust = 0.5))

Let try using interactive plot:

For interactive and animation plot:

c1 <- ggplot(data2, aes(x= dwnom1, y = all_bills, color = Gender)) +
  geom_point(aes(frame = elected)) +
  labs(x ="DW-Nominate score", y = "Number of bills", title = "The DW-Nominate score and Number of bills introduced each year") +
  theme(plot.title = element_text(hjust = 0.5))
## Warning: Ignoring unknown aesthetics: frame
ggplotly(c1)
## Warning in p$x$data[firstFrame] <- p$x$frames[[1]]$data: number of items to
## replace is not a multiple of replacement length

Problem arises when we plot the graph with Color for Different Genders. Similar problem is already indicated in many topics and solution cannot be found. (https://community.plotly.com/t/frame-showing-less-category-than-actual/8628). The Warning message shows that the frame shows less category than normal.

Only female or male are showed (from year 1986).

However. if I alter the option aes(Color = Gender) by aes(ids/label=Gender, text1 = …), the frame can show both Gender at the same time (ofcourse with no Color)

 c2 <- ggplot(data2, aes(x= dwnom1, y = all_bills)) +
  geom_point(aes(frame = elected, ids = Gender, text1 = thomas_name, text2 = seniority)) +
  labs(x ="DW-Nominate score", y = "Number of bills", title = "The DW-Nominate score and Number of bills introduced each year") +
  theme(plot.title = element_text(hjust = 0.5))
ggplotly(c2)

Exercise 3

Explain what you are visualizing here: Drawing a (interactive) line illustrating the number of the bills introduced in congress 110 (all_bills) according to year the member was elected (elected)

Put your figure here:

data3 <- filter(cel, congress == 110)
data3 <- data3 %>% 
  mutate(
    Gender = case_when(
      female == 0 ~ "Male",
      female == 1 ~ "Female"))

ggplot(data3, aes(x= elected, y = all_bills, colour = Gender)) +
  stat_summary(fun.y = mean, geom = "point") +
  stat_summary(fun.y = mean, geom = "line", aes(group = 1)) +
  labs( x= "Elected Year", y = "Mean of bills", title = "The mean value of bills of the members in each elected year") +
  theme_bw() + # theme_bw must be set before theme() to set title in center
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position='none') +
  facet_wrap(~Gender, as.table = FALSE)

Let check some values by filter the “Elected Year” 1990 - 1992 - 1994

library(pastecs)
year1990 <- filter(data3, elected ==1990) 
by(year1990$all_bills, year1990$Gender, stat.desc )
## year1990$Gender: Female
##      nbr.val     nbr.null       nbr.na          min          max        range 
##    2.0000000    0.0000000    0.0000000   31.0000000   36.0000000    5.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   67.0000000   33.5000000   33.5000000    2.5000000   31.7655118   12.5000000 
##      std.dev     coef.var 
##    3.5355339    0.1055383 
## ------------------------------------------------------------ 
## year1990$Gender: Male
##      nbr.val     nbr.null       nbr.na          min          max        range 
##   13.0000000    0.0000000    0.0000000    3.0000000   33.0000000   30.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  150.0000000   11.0000000   11.5384615    2.2999871    5.0112415   68.7692308 
##      std.dev     coef.var 
##    8.2927216    0.7187025
year1992 <- filter(data3, elected ==1992) 
by(year1992$all_bills, year1992$Gender, stat.desc)
## year1992$Gender: Female
##      nbr.val     nbr.null       nbr.na          min          max        range 
##    8.0000000    0.0000000    0.0000000    7.0000000   74.0000000   67.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  213.0000000   20.5000000   26.6250000    7.8078200   18.4625606  487.6964286 
##      std.dev     coef.var 
##   22.0838499    0.8294404 
## ------------------------------------------------------------ 
## year1992$Gender: Male
##      nbr.val     nbr.null       nbr.na          min          max        range 
##   36.0000000    0.0000000    0.0000000    2.0000000   52.0000000   50.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  632.0000000   15.0000000   17.5555556    2.2040527    4.4744649  174.8825397 
##      std.dev     coef.var 
##   13.2243162    0.7532838
year1994 <- filter(data3, elected ==1994) 
by(year1994$all_bills, year1994$Gender, stat.desc)
## year1994$Gender: Female
##      nbr.val     nbr.null       nbr.na          min          max        range 
##    4.0000000    0.0000000    0.0000000   14.0000000   41.0000000   27.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  108.0000000   26.5000000   27.0000000    5.9581876   18.9616123  142.0000000 
##      std.dev     coef.var 
##   11.9163753    0.4413472 
## ------------------------------------------------------------ 
## year1994$Gender: Male
##      nbr.val     nbr.null       nbr.na          min          max        range 
##   26.0000000    0.0000000    0.0000000    1.0000000   53.0000000   52.0000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  355.0000000   11.0000000   13.6538462    2.0523475    4.2268888  109.5153846 
##      std.dev     coef.var 
##   10.4649598    0.7664478
d <- ggplot(data3, aes(x= elected, y = all_bills, colour = Gender)) +
  stat_summary(fun.y = mean, geom = "point") +
  stat_summary(fun.y = mean, geom = "line", aes(group = 1)) +
  labs( x= "Elected Year", y = "Mean of bills", title = "The mean value of bills of the members in each elected year") +
  theme_bw() + # theme_bw must be set before theme() to set title in center
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position='none') +
  facet_wrap(~Gender, as.table = FALSE)  

ggplotly(d)

Drawing box plot to identify median - min - max of these value:

d <- ggplot(data3, aes(x= elected, y = all_bills, colour = Gender)) +
  geom_boxplot()+
  labs( x= "Elected Year", y = "Mean of bills", title = "The mean value of bills of the members in each elected year") +
  theme_bw() + # theme_bw must be set before theme() to set title in center
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position='none') +
  facet_wrap(~Gender, as.table = FALSE)  

ggplotly(d)

A few comments on interactive box plot and line graph: 1. No value on mean of bills is shown on the interactive graph.
2. A box plot only shows the plot for a few years (not all of the data)

As we can see the value of mean(Female) of 1992 and 1994 are 26.625 and 27 but it can’t be shown on the interactive line graph. In my opinion, these values are supposed to be shown.

Exercise 4

Explain what you are visualizing here: Drawing a box plot illustrate the number of bills (all_law) that the member(female) introduced that became law in congress 110

Put your figure here:

data4 <- filter(cel, congress == 110)
data4 <- data4 %>% 
  mutate(
    Gender = case_when(
      female == 0 ~ "Male",
      female == 1 ~ "Female"))
data4 <- data4 %>% 
  mutate(
    Dem = case_when(
      dem == 0 ~ "Republican",
      dem == 1 ~ "Democrat"))

g <- ggplot(data4, aes(x= Dem, y = all_bills, fill = Dem)) +
  geom_boxplot()  +
  labs( x= "Political view", y = "The number of bills", title = "The number of bills introduced in congress 110") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(legend.position='none')+
  facet_wrap(~Gender, as.table = FALSE)
g 

ggplotly(g)